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Prospective Value of Big Data Analysis Method for Assessment of Pharmacotherapy Quality and Efficacy in Patients with Arterial Hypertension

Prospective Value of Big Data Analysis Method for Assessment of Pharmacotherapy Quality and Efficacy in Patients with Arterial Hypertension

Burykin I.M., Aleyeva G.N., Hafizyanova R.H.
Keywords: arterial hypertension; pharmacoepidemiological methods; compliance; big data.
СТМ, 2017, volume 9, issue 4, pages 194-200.

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The aim of the study was to study the capabilities and perspectives of using big data analysis method to assess rationality and quality of pharmacotherapy in patients with arterial hypertension.

Materials and Methods. Analysis of data on supply of medicinal products (MP) to benefit-entitled categories of citizens (federal and regional) was carried out using the software written in Python 3.6 and OLAP-system. Pharmacoepidemiological methods were based on the defined daily dose (DDD) methodology.

Results. Rational pharmacotherapy and high adherence to treatment of arterial hypertension were not recorded in all cases. It was revealed that in the investigated areas of the Republic of Tatarstan, the average of 86 days passed from the initial to the subsequent visit and 25% of patients had the next appointment in 90 days or more in 2013. In addition to MPs for the cardiovascular system pertaining to category C of ATC classification (anatomic therapeutic chemical classification of drugs (the international system)), in 10% of cases, drugs of other categories were administered (acetylsalicylic acid, Piracetam, Cerebrolysin, etc.). Inhibitors of renin-angiotensin system were the most widely used MPs (196 DDD/person/year), while calcium channel antagonists came second in the volume of consumption (50.4 DDD/person/year). There were recorded significant differences in total consumption of antihypertensive MPs between areas (up to 3.5 times).

Conclusion. Big data analysis method is a promising tool to assess rationality and quality of pharmacotherapy providing the possibility to evaluate qualitative and quantitative indicators of pharmacotherapy at the general population level.

  1. Khabriev R.U., Lindendraten A.L., Komarov Yu.M. The strategy of health care of population as a background of public social policy. Problemy sotsial’noy gigieny, zdravookhraneniya i istorii meditsiny 2014; 3: 3–5.
  2. Golikova T.A. On elaboration and adoption of regional programs of healthcare modernization. Menedzhment kachestva v sfere zdravookhraneniya i sotsial’nogo razvitiya 2011; 1: 4–10.
  3. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet 2016; 389(10064): 37–55,
  4. Loewen L., Roudsari A. Evidence for business intelligence in health care: a literature review. Stud Health Technol Inform 2017; 235: 579–583.
  5. Khafiz’yanova R.Kh., Burykin I.M., Aleeva G.N. Matematicheskaya statistika v eksperimental’noy i klinicheskoy farmakologii [Mathematical statistics in experimental and clinical pharmacology]. Kazan: Meditsina; 2006; 374 p.
  6. Ziganshina L.E., Magsumova D.R., Kuchaeva A.V., Pikuza O.I., Gerasimov V.B., Yavorskiy A.N. ATC/DDD classification system in pharmacoepidemiological studies. Kachestvennaya klinicheskaya praktika 2004; 1: 28–33.
  7. Russian Medical Society for Arterial Hypertension, Society of Cardiology of Russian Federation. Diagnosis and treatment of arterial hypertension. Kardiovaskulyarnaya terapiya i profilaktika 2008; 7(6 Suppl 2): 7–34.
  8. Nimmagadda S.L., Dreher H.V. On robust methodologies for managing public health care systems. Int J Environ Res Public Health 2014; 11(1): 1106–1140, .
  9. Khafiz’yanova R.Kh., Aleeva G.N., Burykin I.M. Perspektivy ispol’zovaniya data mining metodov analiza dannykh v meditsine. V kn.: Materialy Mezhdunarodnoy nauchno-prakticheskoy konferentsii “Rol’ nepravitel’stvennykh organizatsiy v reshenii problem, svyazannykh s razrabotkoy i vnedreniem innovatsionnykh tekhnologiy vo vsekh sferakh chelovecheskoy deyatel’nosti”, posvyashchennoy 15-letiyu obrazovaniya Akademii informatizatsii Respubliki Tatarstan (2 chast’) [Perspectives of using data mining methods to analyze medical data. In: Materials of International Scientific and Practical Conference “The role of non-governmental organizations in solving the problems of development and implementation of innovation technologies in all spheres of human activities” devoted to the fifteenth anniversary of the Academy of Informatization of Tatarstan Republic (part 2)]. Kazan; 2010; p. 32–39.
  10. Karmalita E.E., Yur’ev K.L. Consumption of medicinal agents for arterial hypertension management. Ukrainskiy meditsinskiy zhurnal 2007; 61(5): 63–72.
  11. Yakusheva E.N. Possibilities of using defined daily dose system. Vestnik Volgogradskogo gosudarstvennogo meditsinskogo universiteta 2008; 26(2): 74–77.
Burykin I.M., Aleyeva G.N., Hafizyanova R.H. Prospective Value of Big Data Analysis Method for Assessment of Pharmacotherapy Quality and Efficacy in Patients with Arterial Hypertension. Sovremennye tehnologii v medicine 2017; 9(4): 194–200,

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